Soda Core For Modern Data Quality And Observability
Download Soda Core For Modern Data Quality And Observability PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Soda Core For Modern Data Quality And Observability book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Soda Core for Modern Data Quality and Observability
"Soda Core for Modern Data Quality and Observability" In "Soda Core for Modern Data Quality and Observability," readers are expertly guided through the intricate landscape of data quality management and observability in today's dynamic data environments. The book delivers a thorough introduction to the key dimensions of data quality—including accuracy, completeness, timeliness, and consistency—and explores the rise of modern data observability. With careful attention to architectural challenges in distributed data systems and the growing need for quantifiable data quality metrics, it provides a robust foundation for organizations seeking proactive assurance in their data operations. The heart of the book is a comprehensive examination of Soda Core—an advanced, open-source platform for data quality monitoring. Detailed chapters unveil Soda Core's flexible architecture, deployment strategies, and integration capabilities, equipping professionals to define, automate, and manage complex data quality checks at scale. Practical guidance on YAML-driven configuration, dynamic anomaly detection, and seamless integration with orchestration frameworks such as Airflow and dbt empowers teams to implement continuous data assurance across diverse environments, from on-premises infrastructure to the cloud. Beyond technical implementation, this authoritative resource addresses the broader enterprise context, including the operationalization of end-to-end observability, security, compliance automation, and the extensibility of Soda Core through custom plugins and APIs. Real-world industry use cases highlight successful deployments in regulated sectors, modernization projects, and real-time streaming scenarios, while expert insights reveal best practices, anti-patterns, and future trends in data quality engineering. With clear explanations and actionable strategies, this book becomes indispensable for data engineers, architects, and leaders aiming to build resilient, reliable, and trustworthy data ecosystems.
97 Things Every Data Engineer Should Know
Author: Tobias Macey
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-06-11
Take advantage of the sky-high demand for data engineers today. With this in-depth book, current and aspiring engineers will learn powerful, real-world best practices for managing data big and small. Contributors from Google, Microsoft, IBM, Facebook, Databricks, and GitHub share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey from MIT Open Learning, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Projects include: Building pipelines Stream processing Data privacy and security Data governance and lineage Data storage and architecture Ecosystem of modern tools Data team makeup and culture Career advice.
Reinforcement Learning, second edition
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.